SoroushVahidi/scidocs huggingface dataset:
vSlicer: latency-aware virtual machine scheduling via differentiated-frequency CPU slicing
(2012)

Recent advances in virtualization technologies have made it feasible to host multiple virtual machines (VMs) in the same physical host and even the same CPU core, with fair share of the physical resources among the VMs. However, as more VMs share the same core/CPU, the CPU access latency experienced by each VM increases substantially, which translates into longer I/O processing latency perceived by I/O-bound applications. To mitigate such impact while retaining the benefit of CPU sharing, we introduce a new class of VMs called latency-sensitive VMs (LSVMs), which achieve better performance for I/O-bound applications while maintaining the same resource share (and thus cost) as other CPU-sharing VMs. LSVMs are enabled by vSlicer, a hypervisor-level technique that schedules each LSVM more frequently but with a smaller micro time slice. vSlicer enables more timely processing of I/O events by LSVMs, without violating the CPU share fairness among all sharing VMs. Our evaluation of a vSlicer prototype in Xen shows that vSlicer substantially reduces network packet round-trip times and jitter and improves application-level performance. For example, vSlicer doubles both the connection rate and request processing throughput of an Apache web server; reduces a VoIP server's upstream jitter by 62%; and shortens the execution times of Intel MPI benchmark programs by half or more.
CENTURION: Incentivizing multi-requester mobile crowd sensing
(2017)

The recent proliferation of increasingly capable mobile devices has given rise to mobile crowd sensing (MCS) systems that outsource the collection of sensory data to a crowd of participating workers that carry various mobile devices. Aware of the paramount importance of effectively incentivizing participation in such systems, the research community has proposed a wide variety of incentive mechanisms. However, different from most of these existing mechanisms which assume the existence of only one data requester, we consider MCS systems with multiple data requesters, which are actually more common in practice. Specifically, our incentive mechanism is based on double auction, and is able to stimulate the participation of both data requesters and workers. In real practice, the incentive mechanism is typically not an isolated module, but interacts with the data aggregation mechanism that aggregates workers' data. For this reason, we propose CENTURION, a novel integrated framework for multi-requester MCS systems, consisting of the aforementioned incentive and data aggregation mechanism. CENTURION's incentive mechanism satisfies truthfulness, individual rationality, computational efficiency, as well as guaranteeing non-negative social welfare, and its data aggregation mechanism generates highly accurate aggregated results. The desirable properties of CENTURION are validated through both theoretical analysis and extensive simulations.
FEATURE EXTRACTION USING SURF ALGORITHM FOR OBJECT RECOGNITION
(2014)

Video surveillance is active research topic in computer vision research area for humans & vehicles, so it is used over a great extent. Multiple images generated using a fixed camera contains various objects, which are taken under different variations, illumination changes after that the object’s identity and orientation are provided to the user. This scheme is used to represent individual images as well as various objects classes in a single, scale and rotation invariant model.The objective is to improve object recognition accuracy for surveillance purposes & to detect multiple objects with sufficient level of scale invariance.Multiple objects detection& recognition is important in the analysis of video data and higher level security system. This method can efficiently detect the objects from query images as well as videos by extracting frames one by one. When given a query image at runtime, by generating the set of query features and it will find best match it to other sets within the database. Using SURF algorithm find the database object with the best feature matching, then object is present in the query image. Keywords— Image recognition, Query image, Local feature, Surveillance system, SURF algorithm.
Efficient Hierarchical Identity-Based Signature With Batch Verification for Automatic Dependent Surveillance-Broadcast System
(2017)

The automatic-dependent surveillance-broad-cast (ADS-B) is generally regarded as the most important module in air traffic surveillance technology. To obtain better airline security, ADS-B system will be deployed in most airspace by 2020, where aircraft will be equipped with an ADS-B device that periodically broadcasts messages to other aircraft and ground station controllers. Due to the open communication environment, the ADS-B system is subject to a broad range of attacks. To simultaneously implement both integrity and authenticity of messages transmitted in the ADS-B system, Yang et al. proposed a new authentication frame based on the three-level hierarchical identity-based signature (TLHIBS) scheme with batch verification, as well as constructing two schemes for the ADS-B system. However, neither TLHIBS schemes are sufficiently lightweight for practical deployment due to the need for complex hash-to-point operation or expensive certification management. In this paper, we construct an efficient TLHIBS scheme with batch verification for the ADS-B system. Our scheme does not require hash-to-point operation or (expensive) certification management. We then prove the TLHIBS scheme secure in the random oracle model. We also demonstrate the practicality of the scheme using experiments, whose findings indicate that the TLHIBS scheme supports attributes required by the ADS-B system without the computation cost in Chow et al.'s scheme and Yang et al.'s TLHIBS schemes.
Workplace harassment: double jeopardy for minority women.
(2006)

To date there have been no studies of how both sex and ethnicity might affect the incidence of both sexual and ethnic harassment at work. This article represents an effort to fill this gap. Data from employees at 5 organizations were used to test whether minority women are subject to double jeopardy at work, experiencing the most harassment because they are both women and members of a minority group. The results supported this prediction. Women experienced more sexual harassment than men, minorities experienced more ethnic harassment than Whites, and minority women experienced more harassment overall than majority men, minority men, and majority women.
Exposure of Children and Adolescents to Alcohol Marketing on Social Media Websites
(2014)

AIMS
In 2011, online marketing became the largest marketing channel in the UK, overtaking television for the first time. This study aimed to describe the exposure of children and young adults to alcohol marketing on social media websites in the UK.


METHODS
We used commercially available data on the three most used social media websites among young people in the UK, from December 2010 to May 2011. We analysed by age (6-14 years; 15-24 years) and gender the reach (proportion of internet users who used the site in each month) and impressions (number of individual pages viewed on the site in each month) for Facebook, YouTube and Twitter. We further analysed case studies of five alcohol brands to assess the marketer-generated brand content available on Facebook, YouTube and Twitter in February and March 2012.


RESULTS
Facebook was the social media site with the highest reach, with an average monthly reach of 89% of males and 91% of females aged 15-24. YouTube had a similar average monthly reach while Twitter had a considerably lower usage in the age groups studied. All five of the alcohol brands studied maintained a Facebook page, Twitter page and YouTube channel, with varying levels of user engagement. Facebook pages could not be accessed by an under-18 user, but in most cases YouTube content and Twitter content could be accessed by those of all ages.


CONCLUSION
The rise in online marketing of alcohol and the high use of social media websites by young people suggests that this is an area requiring further monitoring and regulation.
An implementation of the FP-growth algorithm
(2005)

The FP-growth algorithm is currently one of the fastest approaches to frequent item set mining. In this paper I describe a C implementation of this algorithm, which contains two variants of the core operation of computing a projection of an FP-tree (the fundamental data structure of the FP-growth algorithm). In addition, projected FP-trees are (optionally) pruned by removing items that have become infrequent due to the projection (an approach that has been called FP-Bonsai). I report experimental results comparing this implementation of the FP-growth algorithm with three other frequent item set mining algorithms I implemented (Apriori, Eclat, and Relim).
Deep Reinforcement Learning for General Video Game AI
(2018)

The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-specific description language. While the competition has seen plenty of interest, it has so far focused on online planning, providing a forward model that allows the use of algorithms such as Monte Carlo Tree Search. In this paper, we describe how we interface GVGAI to the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems. Using this interface, we characterize how widely used implementations of several deep reinforcement learning algorithms fare on a number of GVGAI games. We further analyze the results to provide a first indication of the relative difficulty of these games relative to each other, and relative to those in the Arcade Learning Environment under similar conditions.
Working Memory Capacity as Executive Attention
(2009)

Performance on measures of working memory (WM) capacity predicts performance on a wide range of real-world cognitive tasks. I review the idea that WM capacity (a) is separable from short-term memory, (b) is an important component of general fluid intelligence, and (c) represents a domainfree limitation in ability to control attention. Studies show that individual differences in WM capacity are reflected in performance on antisaccade, Stroop, and dichotic-listening tasks. WM capacity, or executive attention, is most important under conditions in which interference leads to retrieval of response tendencies that conflict with the current task.
Characterizing Horn Antenna Signals for Breast Cancer Detection
(2018)

An in-depth analysis of signals from horn antennas used in breast cancer detection research has been carried out. It was found that following the excitation signal, both the double-ridged and the quad-ridged ultra-wideband horn antennas radiate additional unwanted signals that persistently oscillate. Undesirable signal oscillations sources were identified as the horn antenna cavity resonance and inherent antenna LC resonance. These signals interfere with the tumor’s signal response and need to be eliminated for successful detection of the cancerous growth. This paper proposes solutions to remove or minimize these signals without affecting antenna parameters such as bandwidth, gain, ports isolation, and polarization isolation. Modification of the antenna cavity successfully suppressed the unwanted cavity oscillation. Modification of the antenna waveguide reduced inductance and consequently mitigated LC oscillation. The resulting time and frequency domain horn antenna signals demonstrate the effectiveness of the proposed methods. Finally, a breast phantom with a tumor is simulated using signals from the original and a modified horn antenna. The delay and sum method is used to create images. The breast images demonstrate enhanced image quality through the reduction of clutter using the proposed techniques. The signal-to-clutter ratios are 0.448 and 1.6823 dB for the images produced by using the original and modified antennas, respectively.
Visualising the structure of architectural open spaces based on shape analysis
(2004)

Visualisation and interpretation of architectural spaces are intellectually challenging and multifaceted exercises with fairly wide ranging applications and implications. In the context of urban planning, they are most commonly undertaken to evaluate the usage of the architectural space to ensure efficient navigation and accessibility [1]. These exercises clearly assume a certain influence of the built structures on the human cognition. However, what aspect of architectural space affects the human behaviour still remains an open debate. In this respect, it is closely similar to an exercise to identify the unknown visual variables in information visualization. Since a quantitative analysis of the architectural geometric structure on a large scale will be a daunting computational task, the open space bounded by the built structures is studied instead [2]. The study of architectural open spaces essentially involves the computation of the visibility polygon or isovist (space visible all around a viewpoint, see Figure 1a) from a viewpoint and calculating various shape measures of the visibility polygon. The isovist computation involves drawing rays
Design and Implementation of the LogicBlox System
(2015)

The LogicBlox system aims to reduce the complexity of software development for modern applications which enhance and automate decision-making and enable their users to evolve their capabilities via a ``self-service'' model. Our perspective in this area is informed by over twenty years of experience building dozens of mission-critical enterprise applications that are in use by hundreds of large enterprises across industries such as retail, telecommunications, banking, and government. We designed and built LogicBlox to be the system we wished we had when developing those applications.
 In this paper, we discuss the design considerations behind the LogicBlox system and give an overview of its implementation, highlighting innovative aspects. These include: LogiQL, a unified and declarative language based on Datalog; the use of purely functional data structures; novel join processing strategies; advanced incremental maintenance and live programming facilities; a novel concurrency control scheme; and built-in support for prescriptive and predictive analytics.
Should I use TensorFlow
(2016)

Google’s Machine Learning framework TensorFlow was opensourced in November 2015 [1] and has since built a growing community around it. TensorFlow is supposed to be flexible for research purposes while also allowing its models to be deployed productively [7]. This work is aimed towards people with experience in Machine Learning considering whether they should use TensorFlow in their environment. Several aspects of the framework important for such a decision are examined, such as the heterogenity, extensibility and its computation graph. A pure Python implementation of linear classification is compared with an implementation utilizing TensorFlow. I also contrast TensorFlow to other popular frameworks with respect to modeling capability, deployment and performance and give a brief description of the current adaption of the framework.
Robust Abandoned Object Detection Using Dual Foregrounds
(2008)

As an alternative to the tracking-based approaches that heavily depend on accurate detection of moving objects, which often fail for crowded scenarios, we present a pixelwise method that employs dual foregrounds to extract temporally static image regions. Depending on the application, these regions indicate objects that do not constitute the original background but were brought into the scene at a subsequent time, such as abandoned and removed items, illegally parked vehicles. We construct separate longand short-term backgrounds that are implemented as pixelwise multivariate Gaussian models. Background parameters are adapted online using a Bayesian update mechanism imposed at different learning rates. By comparing each frame with these models, we estimate two foregrounds. We infer an evidence score at each pixel by applying a set of hypotheses on the foreground responses, and then aggregate the evidence in time to provide temporal consistency. Unlike optical flow-based approaches that smear boundaries, our method can accurately segment out objects even if they are fully occluded. It does not require on-site training to compensate for particular imaging conditions. While having a low-computational load, it readily lends itself to parallelization if further speed improvement is necessary.
Learning a Classification Model for Segmentation
(2003)

We propose a two-class classification model for grouping. Human segmented natural images are used as positive examples. Negative examples of grouping are constructed by randomly matching human segmentations and images. In a preprocessing stage an image is oversegmented into superpixels. We define a variety of features derived from the classical Gestalt cues, including contour, texture, brightness and good continuation. Information-theoretic analysis is applied to evaluate the power of these grouping cues. We train a linear classifier to combine these features. To demonstrate the power of the classification model, a simple algorithm is used to randomly search for good segmentations. Results are shown on a wide range of images.
Scene Parsing with Object Instances and Occlusion Ordering
(2014)

This work proposes a method to interpret a scene by assigning a semantic label at every pixel and inferring the spatial extent of individual object instances together with their occlusion relationships. Starting with an initial pixel labeling and a set of candidate object masks for a given test image, we select a subset of objects that explain the image well and have valid overlap relationships and occlusion ordering. This is done by minimizing an integer quadratic program either using a greedy method or a standard solver. Then we alternate between using the object predictions to refine the pixel labels and vice versa. The proposed system obtains promising results on two challenging subsets of the LabelMe and SUN datasets, the largest of which contains 45, 676 images and 232 classes.
Enhancing Neural Disfluency Detection with Hand-Crafted Features
(2016)

In this paper, we apply a bidirectional Long Short-Term Memory with a Conditional Random Field to the task of disfluency detection. Long-range dependencies is one of the core problems for disfluency detection. Our model handles long-range dependencies by both using the Long Short-Term Memory and hand-crafted discrete features. Experiments show that utilizing the hand-crafted discrete features significantly improves the model’s performance by achieving the state-of-the-art score of 87.1% on the Switchboard corpus.
Detection and identification algorithm of the S1 and S2 heart sounds
(2016)

This article attempts to contribute to heart abnormalities detection by suggesting a segmentation algorithm of S1 and S2 heart sounds. It describes the steps followed and the results achieved during the realization of the first PASCAL (Pattern Analysis, Statistical Modelling and Computational Learning) Classifying Heart Sounds Challenge. The performed work leads to improved results, compared to the results found by the three finalists of this challenge, using the envelope of Shannon energy and other methods, to detect and identify first and second heart sound of phonocardiogram signals.
MV3R-Tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries
(2001)

Among the various types of spatio-temporal queries, the most common ones involve window queries in time. In particular, timestamp (or timeslice) queries retrieve all objects that intersect a window at a specific timestamp. Interval queries include multiple (usually consecutive) timestamps. Although several indexes have been developed for either type, currently there does not exist a structure that can efficiently process both query types. This is a significant problem due to the fundamental importance of these queries in any spatio-temporal system that deals with historical information retrieval. Our paper addresses the problem by proposing the MV3R-tree, a structure that utilizes the concepts of multi-version B-trees and 3D-Rtrees. Extensive experimentation proves that MV3R-trees compare favorably with specialized structures aimed at timestamp and interval window queries, both in terms of time and space requirements.
Towards Music Structural Segmentation across Genres: Features, Structural Hypotheses, and Annotation Principles
(2016)

This article faces the problem of how different audio features and segmentation methods work with different music genres. A new annotated corpus of Chinese traditional Jingju music is presented. We incorporate this dataset with two existing music datasets from the literature in an integrated retrieval system to evaluate existing features, structural hypotheses, and segmentation algorithms outside a Western bias. A harmonic-percussive source separation technique is introduced to the feature extraction process and brings significant improvement to the segmentation. Results show that different features capture the structural patterns of different music genres in different ways. Novelty- or homogeneity-based segmentation algorithms and timbre features can surpass the investigated alternatives for the structure analysis of Jingju due to their lack of harmonic repetition patterns. Findings indicate that the design of audio features and segmentation algorithms as well as the consideration of contextual information related to the music corpora should be accounted dependently in an effective segmentation system.
The effect of five prosthetic feet on the gait and loading of the sound limb in dysvascular below-knee amputees.
(1995)

The purpose of this study was to determine the effects of prosthetic foot design on the vertical ground reaction forces experienced by the sound and amputated limbs in a group of persons with dysvascular below-knee amputations. Stride characteristics, joint motion, and ground reaction forces were recorded simultaneously during a self-selected free walking velocity in seven subjects wearing five different prosthetic feet (SACH, Flex-Foot, Carbon Copy II, Seattle, Quantum). Subjects used each foot for one month prior to testing. Results indicated that the sound limb was exposed to higher vertical ground reaction forces than normal despite a reduced walking velocity. Use of the Flex-Foot resulted in the lowest sound limb vertical forces, which appears to be related to its large arc of dorsiflexion motion. In addition, there was increased loading response knee flexion of the sound limb indicating an attempt by these subjects to modulate floor impact. These results suggest that the intact lower extremity is susceptible to excessive floor impact, and that prosthetic foot design can have an effect on the magnitude of the vertical forces experienced by the limb.